Testing randomness extractors
Marton Trencseni - Sun 02 June 2024 • Tagged with randomness, extractors, biased, coin, fair
I apply NIST's suite of statistical tests to my randomness extractor implementations from the previous posts.
Randomness extractors: making fair coins out of biased coins
Marton Trencseni - Sun 26 May 2024 • Tagged with randomness, extractors, biased, coin, fair
I discuss various models of biased bit sequences, and how to extract uniform random (or close to it) output bit sequences from them, illustrated with Python code.
Paper review: A Comparison of Approaches to Advertising Measurement
Marton Trencseni - Wed 01 May 2024 • Tagged with ab-testing, facebook, stratification, propensity
Why are Randomized Controlled Trials (RCTs, known as A/B testing in much of the industry) testing is widely regarded as the golden standard of causal inference? What else can a Data Scientist do if A/B testing is not possible, and why are those alternatives inferior to A/B testing?
This papers shows, using 15 experiments (for ads on Facebook) where a RCT was conducted, that common observational methods (run on the Facebook data, by ignoring the control group) severely mis-estimate the true treatment life (as measured by the RCT), often by a factor of 3x or more. This is true, even though Facebook has (i) very large sample sizes, and, (ii) very high quality data (per-user feature vector) about its users which are used in the observational methods. This should be a major red flag for Data Scientists working on common marketing measurements (such as marketing campaigns) using observational methods.
2024 Data Outlook
Marton Trencseni - Sun 11 February 2024 • Tagged with outlook, 2024, datahub, capm
It is the beginning of the year — a good time to reflect on the previous year and make plans for the year ahead. I wrote this document for my team members in 2024 January to kick off the year. This is an abridged version with sensitive content removed.
Introduction to Marketing Mix Modeling
Marton Trencseni - Sun 23 July 2023 • Tagged with mmm, marketing, mixed, model, lightweight_mmm, google, python
I describe the concept of Marketing Mix Modeling using Google's LightweightMMM library.
Real-world experiments: 5 Lessons from Google, Bing, Netflix and Alibaba
Marton Trencseni - Sun 18 June 2023 • Tagged with ab-testing
I discuss five lessons from large-scale experiments conducted by Google, Bing, Netflix and Alibaba: Kohavi's 1 out of 3 rule, Google's 41 shades of blue, Bing's unexpected big win, Alibaba's personalization experiment and Netflix' movie image personalization.
Conditional Probabilities and Simpson's Paradox
Marton Trencseni - Sun 11 June 2023 • Tagged with probability, statistics, simpsons, paradox
I give examples of "unintuitive" conditional probabilities and discuss Simpson's paradox.
Common patterns in technical interviewing
Marton Trencseni - Sat 01 October 2022 • Tagged with interviewing
I will attempt to enumerate all the categories of questions commonly asked in technical interview loops, and my experience with them.
More Data Scientists should learn SQL
In my experience, many Data Scientists struggle to write SQL queries in interviews.
100 articles
Marton Trencseni - Mon 18 October 2021 • Tagged with meta
A review and introspect on the first 100 articles written on Bytepawn. 
The Full Stack Data Scientist
Marton Trencseni - Fri 23 July 2021 • Tagged with data, fallacies
What are the core skills a data scientist needs to sustainably achieve bottom-line impact, without blocking on external help from other roles?
YOLO object detection architecture
Marton Trencseni - Sat 10 July 2021 • Tagged with yolo, yolov5, vision, object detection
I discuss the YOLO neural network architecture for object detection. 
YOLOv5 object detection experiments
Marton Trencseni - Fri 02 July 2021 • Tagged with yolo, yolov5, vision, object detection
I run object detection experiments with pre-trained YOLOv5 models. 
Predicting party affiliation of US politicians using fasttext
Marton Trencseni - Sun 20 June 2021 • Tagged with statistics, trump, politics, fasttext, twitter
I train a fasttext classifier on 1.2M data points to predict US politicians' party affiliations from their twitter messages. 
Random digits and Benford's law
Marton Trencseni - Sat 29 May 2021 • Tagged with statistics
The post explores the distribution of digits of random and non-random numbers from receipts, verifying Benford's law of first digit distribution.
10 ways to iterate from 0 to 1 with deciles
Marton Trencseni - Fri 14 May 2021 • Tagged with mlflow, tracking
What's the best way to iteratore from 0 to 1 in steps of 0.1 in Python, and what are the potential pitfalls?
Building intuition for p-values and statistical significance
Marton Trencseni - Sun 25 April 2021 • Tagged with ab-testing
This is the transcript of a talk I did on experimentation and A/B testing to give the audience an intuitive understanding of p-values and statistical significance.
Random numbers, the natural logarithm and higher dimensional simplexes
Marton Trencseni - Sat 17 April 2021 • Tagged with bayesian, ab-test
The base $e$ of the natural logarithm shows up in an unexpected place. Let's derive why!
Building a Pytorch Autoencoder for MNIST digits
Marton Trencseni - Thu 18 March 2021 • Tagged with pytorch, autoencoder, mnist
I build an Autoencoder network to categorize MNIST digits in Pytorch.
Training a Pytorch Wasserstein MNIST GAN on Google Colab
Marton Trencseni - Wed 03 March 2021 • Tagged with python, pytorch, torchvision, mnist, gan
I train a Pytorch Wasserstein MNIST GAN on Google Colab to beautiful MNIST digits.
